English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Trinucleotide mutational signatures extracted from cancer genomes provide clues useful in understanding the roles of mutagens and mutagenic mechanisms in cancer development. The lack of a simple method for genome-wide analysis of alterations induced by mutagens hampers the identification of trinucleotide signatures of mutagen exposure and evaluation of their relationships with human cancers. Here, we describe a novel approach to facilitate analysis of chemically induced mutations in bacterial cells by detection of increased frequencies of base substitutions after mutagen exposure, using paired-end overlapping next-generation sequencing. DNA samples from  Salmonella typhimurium  strain TA100, exposed to three alkylating agents, ethylnitrosourea (ENU), methylnitrosourea (MNU), and ethyl methansulphonate (EMS), were analysed. The G:C > A:T mutation frequency was increased in all samples, whereas A:T base pair substitution frequencies were increased specifically in samples exposed to ENU, consistent with previous reports. Mutation patterns in the context of 96 possible trinucleotide formats in these samples exhibited a sharp peak corresponding to an NpCpY consensus sequence, which is similar to the mutational signature of alkylating agents in human cancer. These results indicate that our approach can be useful in facilitating the understanding of mechanisms underlying chemical mutagenicity and for identification of unknown causal mutagens in human cancer.

A genome-wide mutation analysis method enabling high-throughput identification of chemical mutagen signatures